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Title

Model-guided respiratory organ motion prediction of the liver from 2D ultrasound

Authors
Preiswerk, Frank
De Luca, Valeria
Arnold, Patrik
Celicanin, Zarko
Tanner, Christine
Bieri, Oliver
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Published in Medical Image Analysis. 2014, vol. 18, no. 5, p. 740-51
Abstract With the availability of new and more accurate tumour treatment modalities such as high-intensity focused ultrasound or proton therapy, accurate target location prediction has become a key issue. Various approaches for diverse application scenarios have been proposed over the last decade. Whereas external surrogate markers such as a breathing belt work to some extent, knowledge about the internal motion of the organs inherently provides more accurate results. In this paper, we combine a population-based statistical motion model and information from 2d ultrasound sequences in order to predict the respiratory motion of the right liver lobe. For this, the motion model is fitted to a 3d exhalation breath-hold scan of the liver acquired before prediction. Anatomical landmarks tracked in the ultrasound images together with the model are then used to reconstruct the complete organ position over time. The prediction is both spatial and temporal, can be computed in real-time and is evaluated on ground truth over long time scales (5.5 min). The method is quantitatively validated on eight volunteers where the ultrasound images are synchronously acquired with 4D-MRI, which provides ground-truth motion. With an average spatial prediction accuracy of 2.4 mm, we can predict tumour locations within clinically acceptable margins.
Identifiers
PMID: 24835181
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Research group Imagerie per-opératoire multi-modalité en radiologie interventionnelle (949)
Project FNS: CR33I3_143980
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PREISWERK, Frank et al. Model-guided respiratory organ motion prediction of the liver from 2D ultrasound. In: Medical Image Analysis, 2014, vol. 18, n° 5, p. 740-51. https://archive-ouverte.unige.ch/unige:44102

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Deposited on : 2014-12-22

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